Description Usage Arguments Value See Also Examples
Calculates the lambda 1 value for the sparseDC algorithm. The lambda 1 value controls the number of marker genes selected for each cluster in the output from SparseDC. It is calculated as the value of lambda 1 that results in no marker genes being selected when then are no meaningful clusters present in the data. Please see the original manuscript for further details.
1 | lambda1_calculator(pdat1, pdat2, ncluster, alpha1 = 0.5, nboot1 = 1000)
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pdat1 |
The centered data from condition 1, columns should be samples (cells) and rows should be features (genes). |
pdat2 |
The centered data from condition 2, columns should be
samples (cells) and rows should be features (genes). The number of genes
should be the same as |
ncluster |
The number of clusters present in the data. |
alpha1 |
The quantile of the bootstrapped lambda 1 values to use, range is (0,1). The default value is 0.5, the median of the calculated lambda 1 values. |
nboot1 |
The number of bootstrap repetitions used for estimating lambda 1, the default value is 1000. |
The calculated value of lambda 1 to use in the main SparseDC algorithm.
lambda2_calculator
for how to calculate the lambda 2
parameter. sparsedc_cluster
for the main sparse differential
clustering function.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | set.seed(10)
# Select small dataset for example
data_test <- data_biase[1:100,]
# Split data into conditions A and B
data_A <- data_test[ , which(condition_biase == "A")]
data_B <- data_test[ , which(condition_biase == "B")]
# Pre-process the data
pre_data <- pre_proc_data(data_A, data_B, norm = FALSE, log = TRUE,
center = TRUE)
# Calculate the lambda 1 value
lambda1_calculator(pdat1 = pre_data[[1]], pdat2 = pre_data[[2]], ncluster=3,
alpha1 = 0.5, nboot1 = 1000)
# Can also run
# Pre-process the data
pre_data <- pre_proc_data(data_A, data_B, norm = FALSE, log = TRUE,
center = TRUE)
pdata_A <- pre_data[[1]]
pdata_B <- pre_data[[2]]
# Calculate the lambda 1 value
lambda1_calculator(pdat1 = pdata_A, pdat2 = pdata_B , ncluster=3,
alpha1 = 0.5, nboot1 = 1000)
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